title: Classification of bioaccumulative and non-bioaccumulative chemicals using statistical learning approaches.
abstract: The present work aimed at developing in silico models allowing for a reliable prediction of bioaccumulative compounds and non-bioaccumulative compounds based on the definition of Bioconcentration Factor (BCF) using a diverse data set of 238 organic molecules. The partial least squares analysis (PLS), C4.5, support vector machine (SVM), and random forest (RF) algorithms were applied, and their performance classifying these compounds in terms of their quantitative structure-activity relationships (QSAR) was evaluated and verified with 5-fold cross-validation and an independent evaluation data set. The obtained results show that the overall prediction accuracies (Q) of the optimal PLS, C4.5, SVM and RF models are 84.5-87.7% for the internal cross-validation, with prediction accuracy (CO) of 86.3-91.1% in the external test sets, and C4.5 is slightly better than the three other methods which presents a Q of 87.7%, and a CO of 91.1% for the test sets. All these results prove the reliabilities of the in silico models, which should be valuable for the environmental risk assessment of the substances.
authors.affiliation: Key Lab of Mariculture and Biotechnology, Ministry of Agriculture, Dalian Fisheries University, Dalian, China.
authors.forename: Xiuli
authors.initials: X
authors.lastname: Sun
authors.forename: Yan
authors.initials: Y
authors.lastname: Li
authors.forename: Xianjie
authors.initials: X
authors.lastname: Liu
authors.forename: Jun
authors.initials: J
authors.lastname: Ding
authors.forename: Yonghua
authors.initials: Y
authors.lastname: Wang
authors.forename: Hui
authors.initials: H
authors.lastname: Shen
authors.forename: Yaqing
authors.initials: Y
authors.lastname: Chang
mesh_terms: D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D005399:Fishes / Q000378:metabolism; D015386:Hazardous Substances / Q000145:classification* / Q000493:pharmacokinetics*; D021281:Quantitative Structure-Activity Relationship; D014874:Water Pollutants, Chemical / Q000737:chemistry / Q000145:classification / Q000493:pharmacokinetics
chemical_list: D015386:Hazardous Substances; D014874:Water Pollutants, Chemical